By Topic

A novel approach for extraction of cluster patterns from Web Usage Data and its performance analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
G T Raju ; Department of CSE, RNS Institute of Technology, Bangalore, Visvesvaraya Technological University, Karnataka, INDIA ; M V Sudhamani

Majority of the techniques that have been used for pattern discovery from Web Usage Data (WUD) are clustering methods. In e-commerce applications, clustering methods can be used for the purpose of generating marketing strategies, product offerings, personalization and Web site adaptation. A novel Partitional based approach for dynamically grouping Web users based on their Web access patterns using Adaptive Resonance Theory1 Neural Network(ART1 NN) clustering algorithm is presented in this paper. The problem formulation and the proposed ART1 NN clustering methodology have been discussed. Experimental results shows that our ART1 NN clustering approach performs better in terms of intra-cluster and inter-cluster distances compared to K-Means and SOM clustering algorithms.

Published in:

Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on

Date of Conference:

23-24 March 2011